Kernel Adaptive Filtering for Nonlinearity-Tolerant Optical Direct Detection Systems

Aleksejs Udalcovs; Anders Djupsj\"obacka; Deming Liu; Gunnar Jacobsen; Jiajia Chen; Jonas M{\aa}rtensson; Lin Gan; Lu Zhang; Ming Tang; Oskars Ozolins

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Kernel Adaptive Filtering for Nonlinearity-Tolerant Optical Direct Detection Systems

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arxiv 1811.02915 v1 pith:RHXYRMF2 submitted 2018-11-07 eess.SP

Kernel Adaptive Filtering for Nonlinearity-Tolerant Optical Direct Detection Systems

Lu Zhang , Oskars Ozolins , Rui Lin , Aleksejs Udalcovs , Xiaodan Pang , Lin Gan , Richard Schatz , Anders Djupsj\"obacka

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Jonas M{\aa}rtensson Urban Westergren Ming Tang Sonnian Fu Deming Liu Weijun Tong Sergei Popov Gunnar Jacobsen Weisheng Hu Shilin Xiao Jiajia Chen

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keywords adaptivedetectiondirectequalizerfilteringkernellimitneeds

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Kernel adaptive filtering (KAF) is proposed for nonlinearity-tolerant optical direct detection. For 7x128Gbit/s PAM4 transmission over 33.6km 7-core-fiber, KAF only needs 10 equalizer taps to reach KP4-FEC limit (BER@2.2e-4), whereas decision-feedback-equalizer needs 43 equalizer taps to reach HD-FEC limit (BER@3.8e-3).

Kernel Adaptive Filtering for Nonlinearity-Tolerant Optical Direct Detection Systems

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